Stites Edward C, Trampont Paul C, Ma Zhong, Ravichandran Kodi S
Beirne B. Carter Center for Immunology Research and the Department of Microbiology, University of Virginia, Charlottesville, VA 22908, USA.
Science. 2007 Oct 19;318(5849):463-7. doi: 10.1126/science.1144642.
To investigate the unregulated Ras activation associated with cancer, we developed and validated a mathematical model of Ras signaling. The model-based predictions and associated experiments help explain why only one of two classes of activating Ras point mutations with in vitro transformation potential is commonly found in cancers. Model-based analysis of these mutants uncovered a systems-level process that contributes to total Ras activation in cells. This predicted behavior was supported by experimental observations. We also used the model to identify a strategy in which a drug could cause stronger inhibition on the cancerous Ras network than on the wild-type network. This system-level analysis of the oncogenic Ras network provides new insights and potential therapeutic strategies.
为了研究与癌症相关的Ras异常激活,我们开发并验证了一种Ras信号传导的数学模型。基于该模型的预测及相关实验有助于解释为何在癌症中通常仅能发现两类具有体外转化潜力的激活型Ras点突变中的一种。对这些突变体进行基于模型的分析,揭示了一个有助于细胞中总Ras激活的系统水平过程。这一预测行为得到了实验观察结果的支持。我们还利用该模型确定了一种策略,即一种药物对癌性Ras网络的抑制作用强于对野生型网络的抑制作用。对致癌Ras网络的这一系统水平分析提供了新的见解和潜在的治疗策略。